Overview
Machine Learning Engineering is an open book that compiles practical knowledge for ML engineers working on large-scale training and inference systems. It covers hardware selection (accelerators, storage), networking, distributed training strategies, inference optimizations, debugging and operational playbooks.
Key Features
- Broad coverage: system-level guidance from hardware to distributed training and inference.
- Practical scripts and tools: benchmarks, debugging scripts and example configurations to reproduce and diagnose issues.
- Community maintained: long-running project with many contributors and rich discussions.
Use Cases
- Engineering teams building or optimizing large-scale training and inference clusters.
- Educational use as course material or reference for ML engineering best practices.
- Decisions around cloud vs on-prem compute and architecture selection.
Technical Highlights
- Markdown-organized handbook with comparison tables and practical troubleshooting steps.
- Focus on distributed training (SLURM, network optimizations) and inference engineering.
- PDF and online versions available for different consumption scenarios.